Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells722
Missing cells (%)60.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric14
Unsupported9

Alerts

Year is highly overall correlated with Oil and 7 other fieldsHigh correlation
Oil is highly overall correlated with Year and 6 other fieldsHigh correlation
Natural gas is highly overall correlated with Kerosene/jet fuel and 1 other fieldsHigh correlation
Other Primary_x000d_ is highly overall correlated with Year and 8 other fieldsHigh correlation
Total Primaries is highly overall correlated with Year and 7 other fieldsHigh correlation
LPG is highly overall correlated with Kerosene/jet fuel and 2 other fieldsHigh correlation
Gasoline/alcohol is highly overall correlated with Year and 6 other fieldsHigh correlation
Kerosene/jet fuel is highly overall correlated with Oil and 10 other fieldsHigh correlation
Diesel oil is highly overall correlated with Year and 11 other fieldsHigh correlation
Fuel oil is highly overall correlated with Year and 7 other fieldsHigh correlation
Other secondary is highly overall correlated with Year and 5 other fieldsHigh correlation
Non-energy is highly overall correlated with Kerosene/jet fuelHigh correlation
Total Secundaries is highly overall correlated with Year and 9 other fieldsHigh correlation
Total is highly overall correlated with Other Primary_x000d_ and 5 other fieldsHigh correlation
Oil has 40 (76.9%) missing valuesMissing
Natural gas has 34 (65.4%) missing valuesMissing
Coal has 52 (100.0%) missing valuesMissing
Hydroenergy has 52 (100.0%) missing valuesMissing
Nuclear has 52 (100.0%) missing valuesMissing
Firewood has 52 (100.0%) missing valuesMissing
Sugarcane and products has 52 (100.0%) missing valuesMissing
Other Primary_x000d_ has 22 (42.3%) missing valuesMissing
Total Primaries has 20 (38.5%) missing valuesMissing
Electricity has 52 (100.0%) missing valuesMissing
LPG has 2 (3.8%) missing valuesMissing
Gasoline/alcohol has 2 (3.8%) missing valuesMissing
Kerosene/jet fuel has 45 (86.5%) missing valuesMissing
Diesel oil has 12 (23.1%) missing valuesMissing
Fuel oil has 41 (78.8%) missing valuesMissing
Coke has 52 (100.0%) missing valuesMissing
Charcoal has 52 (100.0%) missing valuesMissing
Gases has 52 (100.0%) missing valuesMissing
Other secondary has 9 (17.3%) missing valuesMissing
Non-energy has 23 (44.2%) missing valuesMissing
Total Secundaries has 2 (3.8%) missing valuesMissing
Total has 2 (3.8%) missing valuesMissing
Year is uniformly distributedUniform
Year has unique valuesUnique
Coal is an unsupported type, check if it needs cleaning or further analysisUnsupported
Hydroenergy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nuclear is an unsupported type, check if it needs cleaning or further analysisUnsupported
Firewood is an unsupported type, check if it needs cleaning or further analysisUnsupported
Sugarcane and products is an unsupported type, check if it needs cleaning or further analysisUnsupported
Electricity is an unsupported type, check if it needs cleaning or further analysisUnsupported
Coke is an unsupported type, check if it needs cleaning or further analysisUnsupported
Charcoal is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gases is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-30 07:39:45.479710
Analysis finished2023-07-30 07:40:33.098453
Duration47.62 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:40:33.262238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:40:33.526437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing40
Missing (%)76.9%
Infinite0
Infinite (%)0.0%
Mean-771.99333
Minimum-1897.57
Maximum-39.92
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)23.1%
Memory size548.0 B
2023-07-30T07:40:33.747154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1897.57
5-th percentile-1791.013
Q1-1376.045
median-407.745
Q3-323.9025
95-th percentile-110.474
Maximum-39.92
Range1857.65
Interquartile range (IQR)1052.1425

Descriptive statistics

Standard deviation668.77616
Coefficient of variation (CV)-0.86629784
Kurtosis-1.2201133
Mean-771.99333
Median Absolute Deviation (MAD)283.13
Skewness-0.74735749
Sum-9263.92
Variance447261.55
MonotonicityNot monotonic
2023-07-30T07:40:33.934970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-168.2 1
 
1.9%
-317.76 1
 
1.9%
-397.14 1
 
1.9%
-39.92 1
 
1.9%
-418.35 1
 
1.9%
-1897.57 1
 
1.9%
-325.95 1
 
1.9%
-332.98 1
 
1.9%
-1288.21 1
 
1.9%
-1703.83 1
 
1.9%
Other values (2) 2
 
3.8%
(Missing) 40
76.9%
ValueCountFrequency (%)
-1897.57 1
1.9%
-1703.83 1
1.9%
-1639.55 1
1.9%
-1288.21 1
1.9%
-734.46 1
1.9%
-418.35 1
1.9%
-397.14 1
1.9%
-332.98 1
1.9%
-325.95 1
1.9%
-317.76 1
1.9%
ValueCountFrequency (%)
-39.92 1
1.9%
-168.2 1
1.9%
-317.76 1
1.9%
-325.95 1
1.9%
-332.98 1
1.9%
-397.14 1
1.9%
-418.35 1
1.9%
-734.46 1
1.9%
-1288.21 1
1.9%
-1639.55 1
1.9%

Natural gas
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)94.4%
Missing34
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean-601.96889
Minimum-2679.12
Maximum-49.51
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)34.6%
Memory size548.0 B
2023-07-30T07:40:34.154696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2679.12
5-th percentile-2551.5095
Q1-156.445
median-117.365
Q3-97.725
95-th percentile-52.2215
Maximum-49.51
Range2629.61
Interquartile range (IQR)58.72

Descriptive statistics

Standard deviation975.63255
Coefficient of variation (CV)-1.6207358
Kurtosis0.69146306
Mean-601.96889
Median Absolute Deviation (MAD)28.335
Skewness-1.5734087
Sum-10835.44
Variance951858.87
MonotonicityNot monotonic
2023-07-30T07:40:34.371270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
-105.39 2
 
3.8%
-49.51 1
 
1.9%
-1720.88 1
 
1.9%
-2450.07 1
 
1.9%
-2528.99 1
 
1.9%
-95.81 1
 
1.9%
-83.35 1
 
1.9%
-52.7 1
 
1.9%
-59.42 1
 
1.9%
-140.02 1
 
1.9%
Other values (7) 7
 
13.5%
(Missing) 34
65.4%
ValueCountFrequency (%)
-2679.12 1
1.9%
-2528.99 1
1.9%
-2450.07 1
1.9%
-1720.88 1
1.9%
-161.92 1
1.9%
-140.02 1
1.9%
-134.13 1
1.9%
-130.54 1
1.9%
-128.38 1
1.9%
-106.35 1
1.9%
ValueCountFrequency (%)
-49.51 1
1.9%
-52.7 1
1.9%
-59.42 1
1.9%
-83.35 1
1.9%
-95.81 1
1.9%
-103.47 1
1.9%
-105.39 2
3.8%
-106.35 1
1.9%
-128.38 1
1.9%
-130.54 1
1.9%

Coal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Hydroenergy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Nuclear
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Firewood
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Sugarcane and products
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Other Primary_x000d_
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing22
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean-922.725
Minimum-5589.7
Maximum164
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)30.8%
Memory size548.0 B
2023-07-30T07:40:34.580787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5589.7
5-th percentile-4333.328
Q1-1866.9825
median-96.415
Q3104.75
95-th percentile133.3
Maximum164
Range5753.7
Interquartile range (IQR)1971.7325

Descriptive statistics

Standard deviation1603.4082
Coefficient of variation (CV)-1.7376881
Kurtosis2.5174206
Mean-922.725
Median Absolute Deviation (MAD)220.915
Skewness-1.7461897
Sum-27681.75
Variance2570917.9
MonotonicityNot monotonic
2023-07-30T07:40:34.785270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-329.68 1
 
1.9%
-5318.81 1
 
1.9%
-156.5 1
 
1.9%
-193.11 1
 
1.9%
-167.46 1
 
1.9%
-133.94 1
 
1.9%
-3128.85 1
 
1.9%
-2688.52 1
 
1.9%
-2448.58 1
 
1.9%
-2337.24 1
 
1.9%
Other values (20) 20
38.5%
(Missing) 22
42.3%
ValueCountFrequency (%)
-5589.7 1
1.9%
-5318.81 1
1.9%
-3128.85 1
1.9%
-2688.52 1
1.9%
-2448.58 1
1.9%
-2337.24 1
1.9%
-2163.32 1
1.9%
-2034.79 1
1.9%
-1363.56 1
1.9%
-1012.8 1
1.9%
ValueCountFrequency (%)
164 1
1.9%
136 1
1.9%
130 1
1.9%
129 1
1.9%
120 1
1.9%
107 1
1.9%
106 1
1.9%
105 1
1.9%
104 1
1.9%
100 1
1.9%

Total Primaries
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)96.9%
Missing20
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean-1493.1603
Minimum-6958.37
Maximum4.19
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)44.2%
Memory size548.0 B
2023-07-30T07:40:35.021531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6958.37
5-th percentile-5610.403
Q1-2788.405
median-242.98
Q30.3575
95-th percentile1.9645
Maximum4.19
Range6962.56
Interquartile range (IQR)2788.7625

Descriptive statistics

Standard deviation2016.8868
Coefficient of variation (CV)-1.3507503
Kurtosis0.91653169
Mean-1493.1603
Median Absolute Deviation (MAD)244.955
Skewness-1.3095375
Sum-47781.13
Variance4067832.4
MonotonicityNot monotonic
2023-07-30T07:40:35.259094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.65 2
 
3.8%
-329.68 1
 
1.9%
-6958.37 1
 
1.9%
-4539.46 1
 
1.9%
-3202.21 1
 
1.9%
-2950.51 1
 
1.9%
-2988.88 1
 
1.9%
-5026.42 1
 
1.9%
-3106.87 1
 
1.9%
-2488.5 1
 
1.9%
Other values (21) 21
40.4%
(Missing) 20
38.5%
ValueCountFrequency (%)
-6958.37 1
1.9%
-6324.16 1
1.9%
-5026.42 1
1.9%
-4539.46 1
1.9%
-3202.21 1
1.9%
-3106.87 1
1.9%
-2988.88 1
1.9%
-2950.51 1
1.9%
-2734.37 1
1.9%
-2488.5 1
1.9%
ValueCountFrequency (%)
4.19 1
1.9%
2.08 1
1.9%
1.87 1
1.9%
1.62 1
1.9%
0.65 2
3.8%
0.61 1
1.9%
0.53 1
1.9%
0.3 1
1.9%
-0.39 1
1.9%
-1.42 1
1.9%

Electricity
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

LPG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)100.0%
Missing2
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean174.6894
Minimum12.9
Maximum396.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:40:35.516364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.9
5-th percentile55.87
Q1108.12
median160.7
Q3222.99
95-th percentile326.6885
Maximum396.8
Range383.9
Interquartile range (IQR)114.87

Descriptive statistics

Standard deviation87.204111
Coefficient of variation (CV)0.4991952
Kurtosis0.020937514
Mean174.6894
Median Absolute Deviation (MAD)53.915
Skewness0.67051091
Sum8734.47
Variance7604.5569
MonotonicityNot monotonic
2023-07-30T07:40:35.772775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246.78 1
 
1.9%
143.25 1
 
1.9%
116.67 1
 
1.9%
98.63 1
 
1.9%
90.21 1
 
1.9%
39.63 1
 
1.9%
152.38 1
 
1.9%
104.83 1
 
1.9%
108.84 1
 
1.9%
88.42 1
 
1.9%
Other values (40) 40
76.9%
(Missing) 2
 
3.8%
ValueCountFrequency (%)
12.9 1
1.9%
39.63 1
1.9%
52.18 1
1.9%
60.38 1
1.9%
88.42 1
1.9%
90.21 1
1.9%
97 1
1.9%
98.63 1
1.9%
98.88 1
1.9%
104.83 1
1.9%
ValueCountFrequency (%)
396.8 1
1.9%
376.14 1
1.9%
330.68 1
1.9%
321.81 1
1.9%
311.66 1
1.9%
311.38 1
1.9%
292.21 1
1.9%
269.14 1
1.9%
264.15 1
1.9%
256.16 1
1.9%

Gasoline/alcohol
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)100.0%
Missing2
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean-1434.6366
Minimum-3079
Maximum-15.7
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)96.2%
Memory size548.0 B
2023-07-30T07:40:36.043949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3079
5-th percentile-2451.931
Q1-2131.1075
median-1494.07
Q3-847.5
95-th percentile-150.596
Maximum-15.7
Range3063.3
Interquartile range (IQR)1283.6075

Descriptive statistics

Standard deviation814.27479
Coefficient of variation (CV)-0.56758262
Kurtosis-1.1423213
Mean-1434.6366
Median Absolute Deviation (MAD)644.325
Skewness0.12640751
Sum-71731.83
Variance663043.44
MonotonicityNot monotonic
2023-07-30T07:40:36.312427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2290.74 1
 
1.9%
-2109.73 1
 
1.9%
-2576.7 1
 
1.9%
-2131.76 1
 
1.9%
-2084.96 1
 
1.9%
-2324.7 1
 
1.9%
-2145.11 1
 
1.9%
-2346.9 1
 
1.9%
-2122.31 1
 
1.9%
-2411.07 1
 
1.9%
Other values (40) 40
76.9%
(Missing) 2
 
3.8%
ValueCountFrequency (%)
-3079 1
1.9%
-2576.7 1
1.9%
-2454.01 1
1.9%
-2449.39 1
1.9%
-2411.07 1
1.9%
-2346.9 1
1.9%
-2337.59 1
1.9%
-2324.7 1
1.9%
-2290.74 1
1.9%
-2244.81 1
1.9%
ValueCountFrequency (%)
-15.7 1
1.9%
-125.55 1
1.9%
-143 1
1.9%
-159.88 1
1.9%
-162.34 1
1.9%
-172.25 1
1.9%
-218.12 1
1.9%
-470.39 1
1.9%
-704.46 1
1.9%
-764.79 1
1.9%

Kerosene/jet fuel
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing45
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean-65.134286
Minimum-162.83
Maximum-0.82
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)13.5%
Memory size548.0 B
2023-07-30T07:40:36.531092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-162.83
5-th percentile-152.546
Q1-106.7
median-29.4
Q3-24.745
95-th percentile-7.708
Maximum-0.82
Range162.01
Interquartile range (IQR)81.955

Descriptive statistics

Standard deviation61.417197
Coefficient of variation (CV)-0.94293192
Kurtosis-1.0870419
Mean-65.134286
Median Absolute Deviation (MAD)28.58
Skewness-0.7568859
Sum-455.94
Variance3772.0721
MonotonicityNot monotonic
2023-07-30T07:40:41.695566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.82 1
 
1.9%
-25.71 1
 
1.9%
-128.55 1
 
1.9%
-162.83 1
 
1.9%
-29.4 1
 
1.9%
-84.85 1
 
1.9%
-23.78 1
 
1.9%
(Missing) 45
86.5%
ValueCountFrequency (%)
-162.83 1
1.9%
-128.55 1
1.9%
-84.85 1
1.9%
-29.4 1
1.9%
-25.71 1
1.9%
-23.78 1
1.9%
-0.82 1
1.9%
ValueCountFrequency (%)
-0.82 1
1.9%
-23.78 1
1.9%
-25.71 1
1.9%
-29.4 1
1.9%
-84.85 1
1.9%
-128.55 1
1.9%
-162.83 1
1.9%

Diesel oil
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing12
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean1095.155
Minimum0.86
Maximum5723.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:40:42.059795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.86
5-th percentile19.6085
Q169.5525
median101.12
Q31998.255
95-th percentile4814.893
Maximum5723.21
Range5722.35
Interquartile range (IQR)1928.7025

Descriptive statistics

Standard deviation1572.6124
Coefficient of variation (CV)1.4359725
Kurtosis1.567358
Mean1095.155
Median Absolute Deviation (MAD)92.23
Skewness1.5587231
Sum43806.2
Variance2473109.9
MonotonicityNot monotonic
2023-07-30T07:40:42.476756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
343.72 1
 
1.9%
609.37 1
 
1.9%
16.92 1
 
1.9%
57.96 1
 
1.9%
328.29 1
 
1.9%
987.27 1
 
1.9%
1359.36 1
 
1.9%
1986 1
 
1.9%
2110.04 1
 
1.9%
2035.02 1
 
1.9%
Other values (30) 30
57.7%
(Missing) 12
 
23.1%
ValueCountFrequency (%)
0.86 1
1.9%
16.92 1
1.9%
19.75 1
1.9%
33.87 1
1.9%
35.35 1
1.9%
46.67 1
1.9%
46.72 1
1.9%
50.92 1
1.9%
57.96 1
1.9%
62.78 1
1.9%
ValueCountFrequency (%)
5723.21 1
1.9%
4877.27 1
1.9%
4811.61 1
1.9%
4015.97 1
1.9%
3079.05 1
1.9%
2688.03 1
1.9%
2636.84 1
1.9%
2189.65 1
1.9%
2110.04 1
1.9%
2035.02 1
1.9%

Fuel oil
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)100.0%
Missing41
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean-4.9545455
Minimum-563.87
Maximum436.91
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)9.6%
Memory size548.0 B
2023-07-30T07:40:42.858863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-563.87
5-th percentile-363.41
Q1-24.725
median1.88
Q353.13
95-th percentile312.635
Maximum436.91
Range1000.78
Interquartile range (IQR)77.855

Descriptive statistics

Standard deviation241.32944
Coefficient of variation (CV)-48.708694
Kurtosis3.284215
Mean-4.9545455
Median Absolute Deviation (MAD)34.53
Skewness-0.72951623
Sum-54.5
Variance58239.897
MonotonicityNot monotonic
2023-07-30T07:40:43.254953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.88 1
 
1.9%
5.64 1
 
1.9%
4.7 1
 
1.9%
100.62 1
 
1.9%
188.36 1
 
1.9%
436.91 1
 
1.9%
-16.34 1
 
1.9%
-563.87 1
 
1.9%
-32.65 1
 
1.9%
-16.8 1
 
1.9%
(Missing) 41
78.8%
ValueCountFrequency (%)
-563.87 1
1.9%
-162.95 1
1.9%
-32.65 1
1.9%
-16.8 1
1.9%
-16.34 1
1.9%
1.88 1
1.9%
4.7 1
1.9%
5.64 1
1.9%
100.62 1
1.9%
188.36 1
1.9%
ValueCountFrequency (%)
436.91 1
1.9%
188.36 1
1.9%
100.62 1
1.9%
5.64 1
1.9%
4.7 1
1.9%
1.88 1
1.9%
-16.34 1
1.9%
-16.8 1
1.9%
-32.65 1
1.9%
-162.95 1
1.9%

Coke
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Charcoal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Gases
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Other secondary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)100.0%
Missing9
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1353.8312
Minimum219.08
Maximum2335.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:40:43.674426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum219.08
5-th percentile522.827
Q1626.575
median1489
Q31903.195
95-th percentile2145.75
Maximum2335.65
Range2116.57
Interquartile range (IQR)1276.62

Descriptive statistics

Standard deviation653.67317
Coefficient of variation (CV)0.48283212
Kurtosis-1.5786616
Mean1353.8312
Median Absolute Deviation (MAD)559.44
Skewness-0.18155421
Sum58214.74
Variance427288.61
MonotonicityNot monotonic
2023-07-30T07:40:44.109478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
219.08 1
 
1.9%
1786.58 1
 
1.9%
1834.63 1
 
1.9%
1796.97 1
 
1.9%
1994.57 1
 
1.9%
2020.39 1
 
1.9%
2319.58 1
 
1.9%
1914.43 1
 
1.9%
2048.44 1
 
1.9%
2038.13 1
 
1.9%
Other values (33) 33
63.5%
(Missing) 9
 
17.3%
ValueCountFrequency (%)
219.08 1
1.9%
423.27 1
1.9%
521.53 1
1.9%
534.5 1
1.9%
577.57 1
1.9%
585.53 1
1.9%
604.23 1
1.9%
609.56 1
1.9%
615.43 1
1.9%
621.46 1
1.9%
ValueCountFrequency (%)
2335.65 1
1.9%
2319.58 1
1.9%
2147.4 1
1.9%
2130.9 1
1.9%
2086.15 1
1.9%
2048.44 1
1.9%
2041.76 1
1.9%
2038.13 1
1.9%
2020.39 1
1.9%
1994.57 1
1.9%

Non-energy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing23
Missing (%)44.2%
Infinite0
Infinite (%)0.0%
Mean-94.542414
Minimum-289.17
Maximum234.52
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)40.4%
Memory size548.0 B
2023-07-30T07:40:44.536901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-289.17
5-th percentile-254.16
Q1-230.48
median-138.14
Q32.42
95-th percentile166.414
Maximum234.52
Range523.69
Interquartile range (IQR)232.9

Descriptive statistics

Standard deviation143.01393
Coefficient of variation (CV)-1.512696
Kurtosis-0.40036037
Mean-94.542414
Median Absolute Deviation (MAD)99.24
Skewness0.66316401
Sum-2741.73
Variance20452.985
MonotonicityNot monotonic
2023-07-30T07:40:44.896350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
-230.48 1
 
1.9%
-194.95 1
 
1.9%
-237.9 1
 
1.9%
-289.17 1
 
1.9%
-154.36 1
 
1.9%
97.09 1
 
1.9%
125.5 1
 
1.9%
-231.81 1
 
1.9%
-11.31 1
 
1.9%
-165.65 1
 
1.9%
Other values (19) 19
36.5%
(Missing) 23
44.2%
ValueCountFrequency (%)
-289.17 1
1.9%
-265 1
1.9%
-237.9 1
1.9%
-237.38 1
1.9%
-236.16 1
1.9%
-233.26 1
1.9%
-231.81 1
1.9%
-230.48 1
1.9%
-202.2 1
1.9%
-194.95 1
1.9%
ValueCountFrequency (%)
234.52 1
1.9%
193.69 1
1.9%
125.5 1
1.9%
97.09 1
1.9%
8.01 1
1.9%
7.11 1
1.9%
5.33 1
1.9%
2.42 1
1.9%
-0.89 1
1.9%
-4.41 1
1.9%

Total Secundaries
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)100.0%
Missing2
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean2243.3438
Minimum12.9
Maximum7431.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:40:45.142990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.9
5-th percentile127.8655
Q1940.5
median1491.63
Q33492.895
95-th percentile6044.1895
Maximum7431.85
Range7418.95
Interquartile range (IQR)2552.395

Descriptive statistics

Standard deviation1944.5707
Coefficient of variation (CV)0.86681796
Kurtosis0.24357849
Mean2243.3438
Median Absolute Deviation (MAD)937.135
Skewness1.0313023
Sum112167.19
Variance3781355.2
MonotonicityNot monotonic
2023-07-30T07:40:45.403194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3657 1
 
1.9%
2102.93 1
 
1.9%
2676.5 1
 
1.9%
2253.03 1
 
1.9%
2349.52 1
 
1.9%
2920.53 1
 
1.9%
2159.96 1
 
1.9%
2333.92 1
 
1.9%
2187.19 1
 
1.9%
2736.29 1
 
1.9%
Other values (40) 40
76.9%
(Missing) 2
 
3.8%
ValueCountFrequency (%)
12.9 1
1.9%
105.66 1
1.9%
120.4 1
1.9%
136.99 1
1.9%
157.54 1
1.9%
167.81 1
1.9%
209.26 1
1.9%
475.25 1
1.9%
734.94 1
1.9%
823.04 1
1.9%
ValueCountFrequency (%)
7431.85 1
1.9%
7011.19 1
1.9%
6463.27 1
1.9%
5531.98 1
1.9%
5424.18 1
1.9%
4799.07 1
1.9%
4731.58 1
1.9%
4427.44 1
1.9%
4266.69 1
1.9%
4138.26 1
1.9%

Total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)100.0%
Missing2
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean-240.1948
Minimum-2288.56
Maximum91.78
Zeros0
Zeros (%)0.0%
Negative44
Negative (%)84.6%
Memory size548.0 B
2023-07-30T07:40:45.696095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2288.56
5-th percentile-1774.711
Q1-95.1075
median-26.775
Q3-8.6875
95-th percentile28.222
Maximum91.78
Range2380.34
Interquartile range (IQR)86.42

Descriptive statistics

Standard deviation569.14267
Coefficient of variation (CV)-2.3695046
Kurtosis6.3063715
Mean-240.1948
Median Absolute Deviation (MAD)22.42
Skewness-2.7032524
Sum-12009.74
Variance323923.38
MonotonicityNot monotonic
2023-07-30T07:40:45.950155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 1
 
1.9%
-32.21 1
 
1.9%
-28.1 1
 
1.9%
-37.37 1
 
1.9%
-18.92 1
 
1.9%
-8.98 1
 
1.9%
-8.93 1
 
1.9%
-12.98 1
 
1.9%
5.98 1
 
1.9%
-4.47 1
 
1.9%
Other values (40) 40
76.9%
(Missing) 2
 
3.8%
ValueCountFrequency (%)
-2288.56 1
1.9%
-2158.27 1
1.9%
-1868.5 1
1.9%
-1660.08 1
1.9%
-1099.06 1
1.9%
-991.3 1
1.9%
-468.81 1
1.9%
-236.2 1
1.9%
-200 1
1.9%
-158.22 1
1.9%
ValueCountFrequency (%)
91.78 1
1.9%
59.44 1
1.9%
46.42 1
1.9%
5.98 1
1.9%
4.04 1
1.9%
2.7 1
1.9%
-2.34 1
1.9%
-2.37 1
1.9%
-2.8 1
1.9%
-4.44 1
1.9%

Interactions

2023-07-30T07:40:27.412726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:45.871629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:50.832232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:53.807729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:56.541059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:59.300386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:02.467788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:06.300608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:09.243932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:12.006284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:15.043190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:18.930197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:21.716764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:24.405663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:27.825791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:46.365482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:51.186589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:54.047734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:56.827938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:59.613915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:03.105031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:06.697025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:09.428471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:12.351464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:15.317558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:19.280666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:21.999355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:24.817497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:28.025985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:46.561818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:51.470705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:54.228403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:57.027016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:59.812753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:03.437012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:06.907682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:09.625836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:12.559512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:15.618649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:19.479517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:22.185242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:25.016411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:28.205942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:46.782250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:51.646403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:54.401450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:57.204353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:59.988357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:03.749522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:07.106845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:09.796494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:12.742399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:15.850391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:19.680302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:22.385750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:25.199109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:28.433162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:47.057038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:51.867249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:54.588239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:57.377981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:00.185000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:04.050173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:07.293309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:09.992171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:12.931294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:16.165257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:19.872749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:22.565247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:25.379009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:28.735782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:47.339388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:52.068206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:54.761919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:57.570252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:00.384388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:04.372530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:07.501442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:10.198660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:13.126975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:16.500862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:20.072685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:22.766570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:25.580043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:29.067523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:47.686719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:52.273546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:54.956429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:57.748875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:00.569282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:04.725703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:07.722325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:10.420804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:13.325079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:16.781058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:20.254738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:22.951130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:25.804677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:29.374054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:48.026554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:52.496719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:55.159204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:57.935994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:00.747177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:04.955100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:07.918097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:10.610261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:13.533500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:17.101690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:20.417255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:23.119992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:26.008891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:29.691435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:48.286311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:52.693650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:55.351647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:58.139977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:00.945540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:05.161156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:08.126456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:10.821758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:13.741250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:17.442032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:20.634816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:23.312522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:26.227564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:30.008456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:48.727561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:52.870991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:55.541127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:58.337996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:01.154367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:05.367774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:08.339833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:11.014185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:13.946355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:17.778624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:20.827795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:23.490692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:26.429339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:30.314251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:48.983743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:53.059439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:55.731058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:58.539334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:01.338369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:05.548078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:08.518166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:11.223029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:14.152283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:18.075978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:21.004651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:23.681963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:26.627261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:30.605242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:49.445399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:53.234834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:55.931479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:58.732558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:01.651408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:05.717747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:08.681344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:11.436672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:14.371239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:18.323054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:21.159825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:23.882772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:26.818489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:30.916523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:49.840587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:53.427906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:56.145474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:58.902750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:01.965065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:05.907889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:08.864039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:11.634498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:14.563286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:18.535693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:21.352559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:24.053711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:27.021257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:31.243039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:50.358210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:53.601640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:56.338123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:39:59.111520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:02.254722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:06.118316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:09.068009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:11.833524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:14.761269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:18.719026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:21.544051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:24.235061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:40:27.229686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-30T07:40:46.166560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearOilNatural gasOther Primary_x000d_Total PrimariesLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilOther secondaryNon-energyTotal SecundariesTotal
Year1.000-0.664-0.135-0.875-0.891-0.420-0.860-0.1790.832-0.6270.801-0.0500.971-0.452
Oil-0.6641.0000.2000.3330.874-0.021-0.5381.000-0.7550.7860.1750.357-0.7270.455
Natural gas-0.1350.2001.0000.0540.4350.162-0.0041.000-0.5270.4000.0050.405-0.1420.133
Other Primary_x000d_\n-0.8750.3330.0541.0000.8530.3590.609-1.000-0.7570.600-0.6280.358-0.8680.762
Total Primaries-0.8910.8740.4350.8531.0000.3170.429-0.600-0.8560.881-0.4950.423-0.8970.666
LPG-0.420-0.0210.1620.3590.3171.0000.3650.714-0.507-0.455-0.6880.024-0.377-0.003
Gasoline/alcohol-0.860-0.538-0.0040.6090.4290.3651.0000.571-0.5890.082-0.935-0.194-0.8780.345
Kerosene/jet fuel-0.1791.0001.000-1.000-0.6000.7140.5711.000-0.7710.500-0.2861.000-0.7140.750
Diesel oil0.832-0.755-0.527-0.757-0.856-0.507-0.589-0.7711.000-0.7330.580-0.3180.834-0.506
Fuel oil-0.6270.7860.4000.6000.881-0.4550.0820.500-0.7331.0000.1900.357-0.5730.664
Other secondary0.8010.1750.005-0.628-0.495-0.688-0.935-0.2860.5800.1901.0000.1580.827-0.138
Non-energy-0.0500.3570.4050.3580.4230.024-0.1941.000-0.3180.3570.1581.000-0.1060.330
Total Secundaries0.971-0.727-0.142-0.868-0.897-0.377-0.878-0.7140.834-0.5730.827-0.1061.000-0.543
Total-0.4520.4550.1330.7620.666-0.0030.3450.750-0.5060.664-0.1380.330-0.5431.000

Missing values

2023-07-30T07:40:31.712255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:40:32.324524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:40:32.774268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

731YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
11970NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21971NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31972NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.90-15.70NaNNaNNaNNaNNaNNaNNaNNaN12.90-2.80
41973NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN105.66-125.55NaNNaNNaNNaNNaNNaNNaNNaN105.66-19.89
51974NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN120.40-143.00NaNNaNNaNNaNNaNNaNNaNNaN120.40-22.60
61975NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN136.99-162.34NaNNaNNaNNaNNaNNaNNaNNaN136.99-25.35
71976NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN154.80-159.88NaN0.861.88NaNNaNNaNNaNNaN157.54-2.34
81977NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN162.18-172.25NaNNaN5.64NaNNaNNaNNaNNaN167.81-4.44
91978NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN204.56-218.12NaNNaN4.70NaNNaNNaNNaNNaN209.26-8.86
101979NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN256.16-470.39-0.82NaNNaNNaNNaNNaN219.08NaN475.254.04
731YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
432012-397.14NaNNaNNaNNaNNaNNaN-2337.24-2734.37NaN189.92-2449.39NaN2035.02-16.34NaNNaNNaN2041.76-165.654266.69-1099.06
442013-39.92NaNNaNNaNNaNNaNNaN-2448.58-2488.50NaN185.31-3079.00NaN1945.94NaNNaNNaNNaN1787.48-11.313918.73-1660.08
452014-418.35NaNNaNNaNNaNNaNNaN-2688.52-3106.87NaN163.72-2104.21NaN2189.65-563.87NaNNaNNaN1784.89-231.814138.26-1868.50
462015-1897.57NaNNaNNaNNaNNaNNaN-3128.85-5026.42NaN164.66-1930.91NaN2688.03NaNNaNNaNNaN1820.88125.504799.07-2158.27
472016-325.95-2528.99NaNNaNNaNNaNNaN-133.94-2988.88NaN105.69-2178.85NaN2636.84-32.65NaNNaNNaN1891.9697.094731.58-468.81
482017-332.98-2450.07NaNNaNNaNNaNNaN-167.46-2950.51NaN117.28-2454.01NaN3079.05-16.80NaNNaNNaN2335.65-154.365531.98-43.70
492018-1288.21-1720.88NaNNaNNaNNaNNaN-193.11-3202.21NaN107.88-1823.15NaN4015.97-162.95NaNNaNNaN1300.33-289.175424.18-53.29
502019-1703.83-2679.12NaNNaNNaNNaNNaN-156.50-4539.46NaN52.18-2337.59NaN4811.61NaNNaNNaNNaN2147.40-237.907011.19-103.76
512020-1639.55NaNNaNNaNNaNNaNNaN-5318.81-6958.37NaN97.00-1598.52NaN4877.27NaNNaNNaNNaN1489.00-194.956463.27-2288.56
522021-734.46NaNNaNNaNNaNNaNNaN-5589.70-6324.16NaN127.75-1865.73NaN5723.21NaNNaNNaNNaN1580.88-233.267431.85-991.30